Bayesian probabilistic modelling provides a principled framework for coherent inference and prediction under uncertainty. Approximate inference addresses the key challenge of Bayesian computation, that is, the computation of the intractable posterior distribution and related quantities such as the Bayesian predictive distribution. Significant progress has been made in this field during the past 10 years, which enables a wide application of Bayesian modelling techniques to machine learning tasks in computer vision, natural language processing, reinforcement learning etc.
This tutorial offers a coherent summary of the recent advances in approximate inference. We will start the tutorial with an introduction to the approximate inference concept and the basics in variational inference. Then we will describe the fundamental aspects of the modern approximate inference, including scalable inference, Monte Carlo techniques, amortized inference, approximate posterior design, and optimisation objectives. The connections between these recent advances will also be discussed. Lastly, we will provide application examples of advanced approximate inference techniques to downstream uncertainty estimation and decision-making tasks and conclude with a discussion on future research directions.
Timetable Tutorial part 1: basics of approximate inference (approx. 30min) Coffee break & live Q&A 1 (approx. 10min) Tutorial part 2: advances 1 (approx. 30min) Coffee break & live Q&A 2 (approx. 10min) Tutorial part 3: advances 2 (approx. 30min) Coffee break & live Q&A 3 (approx. 10min) Tutorial part 3: applications (approx. 30min)
Yingzhen Li (Microsoft Research Cambridge)
Yingzhen Li is a senior researcher at Microsoft Research Cambridge. She received her PhD from the University of Cambridge, and previously she has interned at Disney Research. She is passionate about building reliable machine learning systems, and her approach combines both Bayesian statistics and deep learning. Her contributions to the approximate inference field include: (1) algorithmic advances, such as variational inference with different divergences, combining variational inference with MCMC and approximate inference with implicit distributions; (2) applications of approximate inference, such as uncertainty estimation in Bayesian neural networks and algorithms to train deep generative models. She has served as area chairs at NeurIPS/ICML/ICLR/AISTATS on related research topics, and she is a co-organizer of the AABI2020 symposium, a flagship event of approximate inference.
Cheng Zhang (Microsoft Research, Cambridge, UK)
Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.
Related Events (a corresponding poster, oral, or spotlight)
2020 Tutorial: (Track1) Advances in Approximate Inference Q&A »
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